2026-04-14T06:26:26Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/546122025-12-19T20:29:51Zcom_2072_6col_2072_452954

urn:hdl:10230/54612 VProject: creating an AI and simulation system for the study of complex diseases and applying it to lung cancer Romero Hernández, Joel Complex diseases Computational physiology, Deep learning Reinforcement learning in healthcare Lung cancer Tutors: Óscar Cámara Rey, Ricard Solé Vicente Treball de fi de grau en Biomèdica Complex diseases like cancer are one of the most serious sources of suffering and death. Their multifactorial nature and multiscale effects compromise the patient’s health and life, so there is a need for new tools for clinicians to navigate through the intricate search space associated with their treatment. In this context, the present project focuses on the creation of a computational platform to optimise the treatment of complex diseases using AI and simulations. Specifically, the system is applied to study the combination of chemotherapy, radiotherapy and immunotherapy in 4 real patients: 3 cases of non- small cell lung cancer (2 adenocarcinomas and 1 squamous cell carcinoma), and 1 case of small cell lung cancer. The prototype takes as input biomedical images directly from the hospital equipment and converts them into a 3D tissue-labelled point cloud that approx- imates the state of the disease at the beginning and the end of the treatment. Then, it takes quasi-natural language instructions to generate customisable dynamic models of the patient’s disease and treatment, combining methods like cellular automata and diffusion- reaction. These models can be visualised and used to run controlled simulations with the prototype’s graphic interface. Furthermore, the system can automatically parameterise them to replicate the behaviour of the disease and treatment using a genetic algorithm. Finally, the platform can also take instructions to generate customisable Deep Reinforce- ment Learning agents that interact with the patient-specific simulations to search for policies that improve their outcomes, so that this knowledge can be used to help future patients. 2022-10-26T15:33:43Z 2022-10-26T15:33:43Z 2022 info:eu-repo/semantics/bachelorThesis ©Tots els drets reservats info:eu-repo/semantics/openAccess